1. Technical Field
The present invention relates to a method and a filter for reducing, in compression/decompression techniques for the transmission or storage of images, the so-called blocking effect, i.e. the degradation of image information produced by dividing an image into blocks of pixels.
2. Discussion of Related Art
The so-called blocking effect arises and becomes visible if images are transmitted at low bit rates, i.e., bit rates less than 2 Mbits/sec. These bit rates typically occur during transmission over ISDN using S.sub.0 interfaces. In most applications, image-data compression/decompression is carried out in accordance with ITU-T Standard H.261. As a result, however, a correction of errors which lead to the blocking effect can only be performed at the output of the decoder.
The blocking effect arises due to the following circumstances. According to ITU-T Recommendation H.261, an image is divided into a plurality of blocks. The block size is preferably 8.times.8 pixels. For each of these blocks, a two-dimensional discrete cosine transform (DCT) is performed, yielding so-called transform coefficients. These transform coefficients indicate the characteristic of a block in the frequency domain. For image data compression, only those few coefficients which have a value higher than a predefined threshold are transmitted. As a result of the division into 8.times.8-pixel blocks, 64 different coefficients are available per block. These coefficients are additionally quantized. For decoding, the two operations of transformation and quantization must be performed.
An information loss already results from the segmentation into the blocks of 8.times.8 pixels, and since the compression is also accomplished on the basis of these blocks, further information is lost. Because the resulting image is degraded by noise, as the bit rate is reduced, the blocking effect becomes more prominent.
Filters are known which are used to reduce quantization errors, and thus the blocking effect. Such a filter was described in the form of an algorithm based on a two-dimensional 3.times.3 Goussian filter. This filter acts on the pixels at or near the boundary of a block. This is disadvantageous in that, while images with little image information, i.e., images showing few details, are improved, images with much image information, i.e., images showing many details, show no improvement and even are degraded. (From: H. C. Reeve III, J. S. Lim, "Reduction of Blocking Effects in Image Coding", Optical Engineering, Vol. 23, No 1, Feb. 1984, pages 34-37.)
An improvement is obtained with a filter having either a 3.times.1-pixel or a 1.times.3-pixel form. Through the use of such a filter, particularly the block corners are smoothed. The use of such a filter results in an improvement, but details remain blurred in those coded regions which are perpendicular to block boundaries, particularly if these regions show good coding. (From: K. -H. Tzou, "Post-Filtering of Transform-Coded Images", SPIE, Vol. 974, Applications of Digital Image Processing XI, 1988, pages 121-126.)
A similar filter was proposed as a one-dimensional asymmetric filter. An improvement is only obtained if the blocking effect is weak. (From: C. Avril, T. Nguyen-Trong, "Linear Filtering for Reducing Blocking Effects in Orthogonal Transform Image Coding", Journal of Electronic Imaging, Vol. 1(2), April 1992, pages 183-192.)
A further, quite different form of filters are space variant filters. This form of filters uses both the signal information and the noise information to improve the filtering. It also incorporates the masking effect to achieve the improvement. This effect means that strong filtering is performed in low-contrast regions of an image, and weak filtering in high-contrast regions. This is based on the fact that in such high-contrast regions the human eye is less sensitive to noise. The filter used is a one-dimensional low-pass filter, for example. (P. Chan, J. S. Lim, "One-Dimensional Processing for Adaptive Image Restoration", IEEE International Conference on Acoustics, Speech and Signal Processing, San Diego, Calif., March 19-21, 1984, pages 37.3.1-37.3.4.) Such a filter has the disadvantage that in the presence of severe noise the filtering is not efficient enough because the filter cannot sufficiently distinguish between noise and signal in the edge areas. As a result, almost no filtering is performed along the edges. Thus, the drawback of this method manifests itself particularly at the edges if the blocking effect occurs there, and cannot be eliminated.